The Dependency Graph Model

The dependency graph model is an alternative to the commonly accepted theory of universal common ancestry. It explains the pattern of similarities and difference in living through the reuse of modules which are reused between different living things. These modules are related by a dependency graph which explains the apparent nested hierarchy.



AminoGraph is a command line tool that can infer a dependency graph from an amino acid sequence. It is the first freely available tool to investigate dependency graphs, producing interactive HTML reports and machine-readable JSON objects. AminoGraph is the result of a concerted effort to produce useful and interesting graphs for researchers and scientists, helping them better understand similiarities and difference in amino acid sequences.
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The Dependency Graph of Life - Winston Ewert

The original paper proposing the dependency graph model

The hierarchical classification of life has been claimed as compelling evidence for universal common ancestry. However, research has uncovered much data which is not congruent with the hierarchical pattern. Nevertheless, biological data resembles a nested hierarchy sufficiently well to require an explanation. While many defenders of intelligent design dispute common descent, no alternative account of the approximate nested hierarchy pattern has been widely adopted. We present the dependency graph hypothesis as an alternative explanation, based on the technique used by software developers to reuse code among different software projects. This hypothesis postulates that different biological species share modules related by a dependency graph. We evaluate several predictions made by this model about both biological and synthetic data, finding them to be fulfilled.


AminoGraph Analysis of the Auditory Protein Prestin From Bats and Whales Reveals a Dependency-Graph Signal That Is Missed by the Standard Convergence Model - Winston Ewert

An update to the model exploring how it applies to amino acids, with a special application to prestin

Alternative models to the theory of universal common descent have, thus far, been underdeveloped. Our previous work introduced a dependency graph model as an alternative way of explaining the patterns of genetic similarity and diversity among living things. According to this model, different forms of life share similarities because they share function-specific genetic features (modules) that may have dependencies on other genetic features. Here, we introduce a tool (AminoGraph) that infers dependency graphs from protein sequence alignments, and we apply this to prestin, a mammalian auditory protein that requires special modifications for ultrasonic hearing in species that use echolocation. Prestin sequences from some echolocating bats show similarities with prestin sequences from echolocating whales. Conventional analyses interpret this as convergence, not because convergence is known to be evolutionarily feasible, but because this preserves the presumed phylogenetic tree. The AminoGraph analysis of prestin presented here provides an alternative explanation: echolocation is supported by two prestin-modifying modules, one or both of which are seen in all echolocating bats and whales. The reliability of this inference is increased by thorough testing of AminoGraph on generated test data sets where sequences are either unrelated, related by common descent, or related by deployment of modules. In all cases, AminoGraph produces the expected relationships.



BIO-Complexity Presents Better Model than Common Ancestry for Explaining Pattern of Nature - Brian Miller

Brian Miller explains the dependency graph for a lay audience

Ewert's article represents only the first step in evaluating and developing his framework. Still, the significance of this research cannot be overstated. The dependency graph model explains why subsets of the biological data crudely fit a tree pattern and why so much of the data is incongruent. It also makes clear predictions on the results of future studies on the distribution across species of both physical traits and similarities in molecular data. Finally, it should lead to a robust and innovative research program based on the intelligent design framework.


New Paper Demonstrates Superiority of Design Model - Cornelius Hunter

Hunter compares the dependency graph to Copernicus

And just as cosmology has seen a stream of ever improving models, the biological models can also improve. This week a very important model has been proposed in a new paper, authored by Winston Ewert, in the Bio-Complexity journal.


This Could Be One of the Most Important Scientific Papers of the Decade - Jay L. Wile

Wile explains the dependency graph for a lay audience

I just devoured the most recent study published in the journal, and I have to say, it is both innovative and impressive. It represents truly original thinking in the field of biology, and if further research confirms the results of the paper, we might very well be on the precipice of an important advancement in the field of biological taxonomy (the science of classifying living organisms).


Ewert's dependency graph paper - Todd Wood

Wood gives a cautious take on the dependency graph paper

So Ewert's paper just came out, and people are excited about it. I've gotten several emails asking about it, and I have to say that it is definitely the most interesting paper I've seen in Bio-Complexity in a long time. At least that's my personal judgment. Your mileage may vary. Anyway, I'm really gratified to see ID scholars take an interest in this fundamental question.


The Dependency Graph Hypothesis — How It Is Inferred - Andrew Jones

Jones describes the math and philosophy behind the Bayesian inference used in the paper

Finally, one reason why I love this paper is it helps explain why Darwin’s tree of life has been accepted by many reasonable people up to now. The reason is: when there isn’t much data, the simpler model wins by default. But now that we have more biological data than ever before, and now that our own understanding of design/engineering/technology has increased in ways that Darwin never could have imagined, we can begin to be able to see that a slightly more complex model could be a much more powerful explanation.


Response to a Critic: But What About Undirected Graphs? - Andrew Jones

Jones responds to the critique that we already know life is not a tree

One commentator argued that we (the scientific community) already know that life is not well explained by a tree, and says that the leading evolutionary explanation is really a reticulated tree or an undirected graph and therefore it is no wonder the dependency graph model beats a “strawman” model. He further argues that human genetic data fits an undirected graph better than a tree, and so would a dependency graph (or so he predicts). Essentially, an undirected graph is what you get when you allow for species hybridizing as well as species splitting: genetic material merges from more than one branch of the tree. This is kind of like lateral gene transfer, but more extreme.


What is a Dependency Graph? - Cornelius Hunter

Hunter discusses how dependency graphs are used in software development and related fields

A recent paper, authored by Winston Ewert, uses a dependency graph approach to model the relationships between the species. This idea is inspired by computer science which makes great use of dependency graphs.


More on Winston Ewert’s “Dependency Graph of Life” — An Important New Paper - Granville Sewell

Sewell discuses the broader context of software analagies in life

Since 1985 I have been arguing that the evolution of life looks much like the evolution of software and other human technology. My main argument, for example in a 2000 Mathematical Intelligencer article, pointed to the fact that according to the fossil record, major new features (new orders, classes, and phyla) appear abruptly, just as major new features in the development of, for example, my PDE solver, appear abruptly, and for the same reason. You only have to think about what gradual development of new organs, or new systems of organs, would look like, or what the gradual development of major new software features would look like, to understand why they must appear abruptly: intermediate stages would usually have to involve incipient new, but not yet useful, features.


A Suspicious Pattern of Deletions - Andrew Jones

Jones explains the evidence for the dependency graph in terms of apparently large numbers of deletions

Ewert’s hypothesis explains the same data more simply: there never was a bloated ancestor, and those genes weren’t lost so many times. The pattern isn’t best explained by any kind of tree. It is best explained by a dependency graph.


Winston Ewert Unpacks his New ID Model, the Dependency Graph–Pt. 1 - Winston Ewert

Ewert explains the state of debate around the nested hierarchy and common descent

On this episode of ID the Future, guest host Robert J. Marks talks with Dr. Winston Ewert about Ewert’s groundbreaking new hypothesis challenging Darwin’s common descent tree of life. The new model is based on the well-established technique of repurposing software code in different software projects. Ewert, a senior researcher at Biologic and the Evolutionary Informatics Lab, describes the nested hierarchical pattern of life and how any credible theory of life’s origin and diversity must explain it. He then describes how Darwin’s basic theory fits, and doesn’t fit, the pattern, and the various ancillary mechanisms invoked to close the gaps. These patches include horizontal gene transfer, convergent evolution, and incomplete lineage sorting. Ewert then cues up what he argues is a better, more elegant hypothesis, the common design hypothesis laid out in his peer-reviewed technical paper.


Dependency Graph, Pt. 2: Winston Ewert Defends His Groundbreaking New ID Model - Winston Ewert

Ewert explains his model and the evidence for it

On this episode of ID the Future, Dr. Winston Ewert continues unpacking his new hypothesis challenging Darwin’s tree of life. Ewert is a software engineer, and his new model is inspired by the coder strategy of repurposing existing code, called modules, for different projects. Moreover, some of these modules depend on other modules, meaning you can generate a dependency graph to better understand the similarities and differences among software programs that share modules. Ewert argues that a dependency graph model better explains the pattern of similarities and differences in the history of life, better than a model of common descent by unguided evolution. As he also explains, the new model is testable in multiple ways.


Ewert’s Dependency Graph Proposal: A Forest of Compositions - Andrew Jones

Jones related the dependency graph to the composition over inheritance principle from software engineering

This realization, if confirmed, would be highly illuminating for biology. Ewert suggests that the dependency graph explains real software data and real biological data, and far better than any inheritance tree. In other words, the dependency graph idea explains the signal that looks a bit like a tree of ancestry, but in terms of composition rather than inheritance. It also goes much further, predicting that many genes follow a kind of “inheritance” (for want of a better word) that has nothing to do with taxonomy. Those predictions have been borne out so far.


New Paper: Common Design Trounces Common Descent for Diversity of Life - Sean Pitman

Pitman explains the Dependency Graph paper

In mid-2018 Dr. Winston Ewert, a computer scientist from Baylor University with expertise in artificial intelligence, programming languages and theory of computation, published a most interesting paper on this concept entitled “The Dependency Graph of Life” in the journal Bio-Complexity (Ewert W., July 31, 2018). In this paper he proposed a new competing pattern to explain the genetic elements of life in comparison to the common descent pattern. The pattern he proposed, ironically, is based on computer software development based on “common design” rather than “common descent”. And, in comparing these well-known patterns to the common descent pattern, he discovered a much much better fit to the genetic data.